Framework for Fuzzy Rule Based Automatic Intrusion Response Selection System (FRAIRSS) using Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Singh, Dileep Kumar; Kaushik, Praveen

doi:10.3233/jifs-18350

Cited by 8 publications

(6 citation statements)

References 11 publications

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“…The applications which use MCDM techniques in the context of IDS are the comparison of feature selection strategies, classifier selection based on TOPSIS, AHP, and so forth. In Reference 18, Singh and Kaushik developed a new framework for intrusion response selection mechanism using fuzzy TOPSIS and fuzzy AHP.…”

Section: Related Workmentioning

confidence: 99%

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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Nowadays, intrusion detection systems (IDSs) enabled with computational intelligence for electing the suitable classifiers to learn the patterns of various types of network attacks are under study. Selection mechanism of single or ensemble classifiers based on performance and ranking is to be enabled in the IDS framework to make accurate pattern predictions. A new anomaly based network intrusion detection system framework is proposed using context adaptive classification through the technique for order of preference by similarity to ideal solution (TOPSIS) ranking mechanism with hierarchy based chi-square and bat algorithms for feature selection.Parameters like accuracy, false positive rate (FPR), and classification model building time are explored for choosing the best and situation aware classifiers using TOPSIS.For experimentation, NSL-KDD and UNSW-NB15 datasets are used. The proposed system opts decision tree (DT) algorithm for NSL-KDD and produces 98.77% accuracy, 0.03% FPR with 8 features. Ensemble learner is selected for the UNSW-NB15 dataset using DT and support vector machine classifiers with 9 features. Combined classifier predicts 89.43% accuracy and 3.215% FPR. Experimentation on the proposed methodology with the state of the art approaches produces promising results.

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Section: Related Workmentioning

confidence: 99%

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Devprasad¹,

Ramanujam

Rajendran³

2022

Concurrency and Computation

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“…The detection model proposed by Su et al [ 11 ] relies on XGBoost to obtain high detection accuracy. A fuzzy rule-based automatic intrusion detection system [ 20 ] is proposed as a solution to deal with precise measurement and uncertainty in the judgment of each criterion. Furthermore, fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is used for response prioritization in multicriteria decision-making.…”

Section: Related Workmentioning

confidence: 99%

A Few‐Shot Learning‐Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data

Wang

Tian

Qin

et al. 2021

Computational Intelligence and Neuroscience

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Network intrusion detection remains one of the major challenges in cybersecurity. In recent years, many machine-learning-based methods have been designed to capture the dynamic and complex intrusion patterns to improve the performance of intrusion detection systems. However, two issues, including imbalanced training data and new unknown attacks, still hinder the development of a reliable network intrusion detection system. In this paper, we propose a novel few-shot learning-based Siamese capsule network to tackle the scarcity of abnormal network traffic training data and enhance the detection of unknown attacks. In specific, the well-designed deep learning network excels at capturing dynamic relationships across traffic features. In addition, an unsupervised subtype sampling scheme is seamlessly integrated with the Siamese network to improve the detection of network intrusion attacks under the circumstance of imbalanced training data. Experimental results have demonstrated that the metric learning framework is more suitable to extract subtle and distinctive features to identify both known and unknown attacks after the sampling scheme compared to other supervised learning methods. Compared to the state-of-the-art methods, our proposed method achieves superior performance to effectively detect both types of attacks.

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“…The likelihood of having inconsistent ratios within the given ranges is therefore far greater [38]. Additionally, to expect the decision-maker to provide (or redo) comparisons such that the ranges include only consistent comparison ratios would be highly unrealistic, as it is a labor-intensive task [39]. The critical gap is thus not only needed for a consistency test to accept only consistent matrices, but also a mechanism to filter out inconsistent information within a consistent matrix [40].…”

Section: The Evolution Of Fahp Theorymentioning

confidence: 99%

A Fuzzy Analytic Hierarchy Process-Based Analysis of the Dynamic Sustainable Management Index in Leisure Agriculture

Lin

2020

Sustainability

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Agricultural leisure is one of the development trends in modern society. However, in leisure agriculture management, managers must consider the dual characteristics of agricultural production technology and the leisure service industry. Therefore, managers often face various selection pressures during management decision-making. This study examined the following five evaluation aspects: organization and operation, leisure agricultural resources, environmental maintenance management, public facility maintenance management, and operational performance. Thereafter, according to the five evaluation aspects, 21 subprojects were further divided at the second level on the basis of different attributes. Fuzzy analytic hierarchy process (FAHP) was adopted to calculate the relative weights of various indicators, and the indicators were dynamically sorted according to their importance. Moreover, the study summarized the dynamic management indicators of leisure agriculture and contributed to research on leisure farming. The results show that managers’ approach toward leisure agriculture management can be divided into three types: active, moderate, and conservative. The indicators in which the active managers attach importance when managing leisure agricultural enterprises are clearly different from those valued by moderate and conservative managers. Finding managers who are suitable for leisure agriculture management is of great significance to leisure agriculture enterprises. Appointing managers with appropriate attitudes who can continuously improve the efficiency of enterprises and create a competitive advantage can bring sustainable business value to these enterprises.

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Framework for Fuzzy Rule Based Automatic Intrusion Response Selection System (FRAIRSS) using Fuzzy Analytic Hierarchy Process and Fuzzy TOPSIS

Cited by 8 publications

References 11 publications

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

Context adaptive ensemble classification mechanism with multi‐criteria decision making for network intrusion detection

A Few‐Shot Learning‐Based Siamese Capsule Network for Intrusion Detection with Imbalanced Training Data

A Fuzzy Analytic Hierarchy Process-Based Analysis of the Dynamic Sustainable Management Index in Leisure Agriculture

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